Mahout in Action

Description

Summary

Mahout in Action is a hands-on introduction to machine learning with Apache Mahout. Following real-world examples, the book presents practical use cases and then illustrates how Mahout can be applied to solve them. Included is a free audio- and video-enhanced ebook.

 About the Technology

A computer system that learns and adapts as it collects data can be really powerful. Mahout, Apache's open source machine learning project, captures the core algorithms of recommendation systems, classification, and clustering in ready-to-use, scalable libraries. With Mahout, you can immediately apply to your own projects the machine learning techniques that drive Amazon, Netflix, and others.

 About this Book

This book covers machine learning using Apache Mahout. Based on experience with real-world applications, it introduces practical use cases and illustrates how Mahout can be applied to solve them. It places particular focus on issues of scalability and how to apply these techniques against large data sets using the Apache Hadoop framework.

This book is written for developers familiar with Java -- no prior experience with Mahout is assumed.

 What's Inside
* Use group data to make individual recommendations
* Find logical clusters within your data
* Filter and refine with on-the-fly classification
* Free audio and video extrasTable of Contents
* Meet Apache Mahout PART 1 RECOMMENDATIONS
* Introducing recommenders
* Representing recommender data
* Making recommendations
* Taking recommenders to production
* Distributing recommendation computations PART 2 CLUSTERING
* Introduction to clustering
* Representing data
* Clustering algorithms in Mahout
* Evaluating and improving clustering quality
* Taking clustering to production
* Real-world applications of clustering PART 3 CLASSIFICATION
* Introduction to classification
* Training a classifier
* Evaluating and tuning a classifier
* Deploying a classifier
* Case study: Shop It To Me

CONTENTS:

Table of Contents

Meet Apache Mahout

PART 1 RECOMMENDATIONS

Introducing recommenders

Representing recommender data

Making recommendations

Taking recommenders to production

Distributing recommendation computations

PART 2 CLUSTERING

Introduction to clustering

Representing data

Clustering algorithms in Mahout

Evaluating and improving clustering quality

Taking clustering to production

Real-world applications of clustering

PART 3 CLASSIFICATION

Introduction to classification

Training a classifier

Evaluating and tuning a classifier

Deploying a classifier

Case study: Shop It To Me